TRACE：面向结构的字符编码用于鲁棒且可泛化的文档水印
TRACE: Structure-Aware Character Encoding for Robust and Generalizable Document Watermarking
AI总结：
TRACE通过扩散模型实现结构感知的局部字符编码，提升文档水印的鲁棒性和泛化能力，实验显示其在PSNR和提取精度上优于现有方法。
摘要：
我们提出TRACE，一种利用扩散模型进行局部字符编码的结构感知框架，通过自适应扩散初始化、引导扩散编码和掩码区域替换三个核心组件，实现对字符结构的稳定表示，从而增强水印的抗干扰能力。实验表明，TRACE在PSNR上比现有方法提高超过5 dB，在跨媒体传输后提取精度更高，且适用于多种语言和字体，具备广泛的实际应用价值。
英文摘要：
We propose TRACE, a structure-aware framework leveraging diffusion models for localized character encoding to embed data. Unlike existing methods that rely on edge features or pre-defined codebooks, TRACE exploits character structures that provide inherent resistance to noise interference due to their stability and unified representation across diverse characters. Our framework comprises three key components: (1) adaptive diffusion initialization that automatically identifies handle points, target points, and editing regions through specialized algorithms including movement probability estimator (MPE), target point estimation (TPE) and mask drawing model (MDM), (2) guided diffusion encoding for precise movement of selected point, and (3) masked region replacement with a specialized loss function to minimize feature alterations after the diffusion process. Comprehensive experiments demonstrate \name{}'s superior performance over state-of-the-art methods, achieving more than 5 dB improvement in PSNR and 5\% higher extraction accuracy following cross-media transmission. \name{} achieves broad generalizability across multiple languages and fonts, making it particularly suitable for practical document security applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
